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Plots for Gross Photosynthesis - Cabral:
- Cabral is significant for silicate at linear (0.04) and nonlinear
(0.02)
- Cabral is only significant for SGD Dils in the nonlinear model
(0.02)
- With removing an outlier point in the last dilution, Cabral is
significant for GP both linear (0.015) and nonlinear (0.015)
# mutate(ammonia_umolL = ifelse(ammonia_umolL = "<0.02", 0.01, ammonia_umolL)) %>%
###################
## Silicate
####################
### confirm linear model
GP_Cabral_silicate_linearmodel <- lmer(umol.cm2.hr ~ log(silicate_umolL+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_silicate_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(silicate_umolL + 1) 0.056267 0.056267 1 46.348 4.0643 0.04962 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### confirm nonlinear model
GP_Cabral_silicate_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(silicate_umolL+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_silicate_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(silicate_umolL + 1), 2) 0.10844 0.054218 2 46.553 4.1984 0.02107
##
## poly(log(silicate_umolL + 1), 2) *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### both are significant so can add either trend line
GP_Cabral_Silicate <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=silicate_umolL,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
geom_smooth(method="lm", formula = "y~log(x)", color="black") +
# geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log silicate",
y = "Rate (umol O2 cm2 hr-1)",
title = "GP Rates for Cabral with Silicate") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_Cabral_Silicate
###################
## SGD dils
####################
### confirm a linear model
GP_Cabral_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.045966 0.045966 1 46.338 3.266 0.07722 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### confirm a nonlinear model
GP_Cabral_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.10382 0.051908 2 45.272 3.9695 0.0258 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GP_Cabral_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "GP Rates for Cabral with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_Cabral_SGDDils
###################
## SGD dils WITHOUT POTENTIAL OUTLIERS
####################
GP_Cabral_SGDDils_nooutlier <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(sample_ID!= "Cabral_Col6_Dil9_Light") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9)) +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
#geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
geom_smooth(method="lm", formula = "y~log(x)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "GP Rates for Cabral with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_Cabral_SGDDils_nooutlier
### confirm a linear model
GP_Cabral_SGD_linearmodel_nooutlier <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(sample_ID!= "Cabral_Col6_Dil9_Light") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_SGD_linearmodel_nooutlier)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.081847 0.081847 1 50 6.2821 0.01549 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### confirm a nonlinear model
GP_Cabral_SGD_nonlinearmodel_nooutlier <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="GP") %>%
filter(sample_ID!= "Cabral_Col6_Dil9_Light") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Cabral_SGD_nonlinearmodel_nooutlier)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.11464 0.057321 2 49 4.5402 0.01553 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plots for Gross Photosynthesis - Varari
- Varari is ONLY significant for silicate in nonlinear model
(0.02)
- Varari has no significant relationship with SGD and GP
###################
## Silicate
####################
### confirm linear model
GP_Varari_silicate_linearmodel <- lmer(umol.cm2.hr ~ log(silicate_umolL+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Varari_silicate_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(silicate_umolL + 1) 0.015463 0.015463 1 62.55 0.7003 0.4059
### confirm nonlinear model
GP_Varari_silicate_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(silicate_umolL+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Varari_silicate_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(silicate_umolL + 1), 2) 0.15527 0.077633 2 61.577 3.925 0.02487
##
## poly(log(silicate_umolL + 1), 2) *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### ONLY significant in nonlinear model
GP_Varari_Silicate <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=silicate_umolL,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~log(x)", color="black") +
geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log silicate",
y = "Rate (umol O2 cm2 hr-1)",
title = "GP Rates for Varari with Silicate") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_Varari_Silicate
###################
## SGD dils
####################
### confirm a linear model
GP_Varari_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Varari_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.010297 0.010297 1 60.065 0.4631 0.4988
### confirm a nonlinear model
GP_Varari_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(GP_Varari_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.089681 0.044841 2 59.102 2.1119 0.13
GP_Varari_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="GP") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "GP Rates for Varari with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_Varari_SGDDils
Plots for Respiration - Cabral:
- There is no significance at Cabral for any environmental parameter
or broad SGD dilutions
###################
## SGD dils
####################
### confirm a linear model
R_Cabral_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(R_Cabral_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 9.037e-08 9.037e-08 1 46.085 0 0.9962
### confirm a nonlinear model
R_Cabral_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(R_Cabral_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.011554 0.0057771 2 45.076 1.5607 0.2211
R_Cabral_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "R Rates for Cabral with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
R_Cabral_SGDDils
Plots for Respiration - Varari
- There is no significance at Varari for any environmental parameter
or broad SGD dilutions
###################
## SGD dils
####################
### confirm a linear model
R_Varari_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(R_Varari_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.0066251 0.0066251 1 59.962 2.1962 0.1436
### confirm a nonlinear model
R_Varari_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(R_Varari_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.012828 0.0064138 2 58.984 2.1691 0.1233
R_Varari_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="R") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "R Rates for Varari with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
R_Varari_SGDDils
Plots for Calcification - Cabral:
- Cabral was allegedly significant for temperature (0.01) and pH
(0.01) with a nonlinear relationship - but not seeing that here for
temp, only for pH
- Cabral is NOT significant for SGD Dils
###################
## pH
####################
### confirm nonlinear model
C_Cabral_pH_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(new_pH+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Cabral_pH_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(new_pH + 1), 2) 0.043887 0.021943 2 47.288 3.2971 0.04566 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
C_Cabral_pH <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=new_pH,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~log(x)", color="black") +
geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log pH",
y = "Rate (umol O2 cm2 hr-1)",
title = "C Rates for Cabral with pH") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
C_Cabral_pH
###################
## SGD dils
####################
### confirm a linear model
C_Cabral_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Cabral_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.015433 0.015433 1 46.137 2.1383 0.1504
### confirm a nonlinear model
C_Cabral_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Cabral_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.01618 0.0080902 2 45.126 1.0993 0.3418
C_Cabral_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Cabral") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
#geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "C Rates for Cabral with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
C_Cabral_SGDDils
Plots for Calcification - Varari
- Varari has a significant linear and nonlinear relationship with
phosphate (<0.0001) and (0.0002) and TA (0.001)
- Varari has a significant … relationship with SGD
###################
## Phosphate
####################
### confirm nonlinear model
C_Varari_phosphate_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(phosphate_umolL+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
filter(phosphate_umolL<1) %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Varari_phosphate_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## poly(log(phosphate_umolL + 1), 2) 0.16319 0.081594 2 60.327 7.2454
## Pr(>F)
## poly(log(phosphate_umolL + 1), 2) 0.001513 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
C_Varari_Phosphate <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
filter(phosphate_umolL<1) %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=phosphate_umolL,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
# geom_smooth(method="lm", formula = "y~log(x)", color="black") +
geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
# geom_hline(yintercept = 0) +
labs(x = "log phosphate",
y = "Rate (umol O2 cm2 hr-1)",
title = "C Rates for Varari with Phosphate") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
C_Varari_Phosphate
###################
## TA
####################
### confirm a nonlinear model
C_Varari_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(TA_initial+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(TA_initial<2550) %>% #### IF CHOOSE TO REMOVE HIGH POINTS, ONLY SIGNIFICANT IN NONLINEAR (otherwise significant in linear too)
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Varari_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(TA_initial + 1), 2) 0.10522 0.052612 2 59.782 3.6216 0.03276 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
C_Varari_TA <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
# filter(TA_initial<2550) %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=TA_initial+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
geom_smooth(method="lm", formula = "y~poly(log(x),2)", color="black") +
geom_hline(yintercept = 0) +
labs(x = "log TA",
y = "Rate (umol O2 cm2 hr-1)",
title = "C Rates for Varari with TA (Minus the potential outliers)") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
C_Varari_TA
###################
## SGD dils
####################
### confirm a linear model
C_Varari_SGD_linearmodel <- lmer(umol.cm2.hr ~ log(SGD_number+1) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Varari_SGD_linearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.11101 0.11101 1 60.079 7.3097 0.008907 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### confirm a nonlinear model
C_Varari_SGD_nonlinearmodel <- lmer(umol.cm2.hr ~ poly(log(SGD_number+1),2) + (1|new_colonynumber), data=RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)))
anova(C_Varari_SGD_nonlinearmodel)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(log(SGD_number + 1), 2) 0.12033 0.060163 2 59.118 3.9338 0.02489 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
C_Varari_SGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(site=="Varari") %>%
filter(P_R=="C") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number+1,
y=umol.cm2.hr)) +
geom_point(size=4, aes(color=as.factor(new_colonynumber))) +
scale_color_manual(values=pnw_palette("Bay", n=9),
guide="none") +
theme_bw() +
coord_trans(x ="log") +
theme(strip.background = element_rect(fill = "white")) +
geom_smooth(method="lm", formula = "y~log(x)", color="black") +
geom_hline(yintercept = 0) +
labs(x = "log SGD Dils",
y = "Rate (umol O2 cm2 hr-1)",
title = "NEC Rates for Varari with SGD Dils") +
theme(axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
C_Varari_SGDDils
Plot how many corals are dead vs alive
Net_CalcvsDissolution <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(P_R=="C") %>%
mutate(SGD_number = as.character(SGD_number),
rate_category = ifelse(umol.cm2.hr > 0, "positive", "negative")) %>%
filter(!is.infinite(umol.cm2.hr)) %>%
ggplot(aes(x=SGD_number,
y=umol.cm2.hr,
fill=rate_category)) +
geom_col(width = 0.8, colour = NA) +
#scale_fill_manual(values = ifelse (umol.cm2.hr > 0, "red", "black")) +
scale_fill_manual(values=c(positive='black',negative='red')) +
theme_bw() +
geom_hline(yintercept = 0) +
facet_wrap(~site) +
#coord_trans(x ="log") +
labs(x = "SGD values",
y = "Rate (umol O2 cm2 hr-1)",
title = "Net Calcifying vs Net Dissolving Corals per SGD Dilution") +
theme(axis.text.x=element_text(size=14),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
Net_CalcvsDissolution
Plot GP:R as a function of SGD
- Cabral is significant (0.025) but Varari is not
## First make new df
GP_RRatio_bySGDDils <- RespoR_Normalized_Full_withNEC_nutrients %>%
dplyr::select(!"sample_ID") %>%
filter(!is.infinite(umol.cm2.hr)) %>%
group_by(P_R, SGD_number, date, site, new_colonynumber) %>%
filter(P_R!="C", P_R!="NP") %>%
#summarize(GP_R = GP/R)
pivot_wider(names_from = P_R, values_from = umol.cm2.hr) %>%
mutate(GP_R = GP/R)
### plots
GP_R_bySGD_bothsites <- GP_RRatio_bySGDDils %>%
#filter(site=="Varari") %>%
mutate(new_colonynumber= as.factor(new_colonynumber)) %>%
ggplot(aes(x=(SGD_number+1),
y=GP_R)) +
geom_point(size=3, aes(color=new_colonynumber)) +
theme_bw() +
coord_trans(x="log") +
facet_wrap(~site, scales = "free_y") +
geom_smooth(method="lm", formula = "y~log(x)") +
labs(x = "SGD Dilutions",
y = "Gross Photosynthesis/Respiration Rates",
title = "SGD by GP:R Relationship for Varari") +
theme(axis.text.x=element_text(size=12),
axis.text.y=element_text(size=18),
axis.title.x=element_text(size=18),
axis.title.y=element_text(size=18),
plot.title=element_text(hjust=0.5, ## centers title
size=20))
GP_R_bySGD_bothsites
## model testing
model_GP_R_SGD_Varari <- lmer(GP_R ~ log(SGD_number+1) + (1|new_colonynumber), data = GP_RRatio_bySGDDils %>%
filter(site=="Varari"))
anova(model_GP_R_SGD_Varari)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.26869 0.26869 1 60.003 3.6316 0.06148 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model_GP_R_SGD_Cabral <- lmer(GP_R ~ log(SGD_number+1) + (1|new_colonynumber), data = GP_RRatio_bySGDDils %>%
filter(site=="Cabral"))
anova(model_GP_R_SGD_Cabral)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## log(SGD_number + 1) 0.28731 0.28731 1 46.031 5.3534 0.0252 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Make correlation matrix for nutrients
## data
corr_matrix_envparams <- RespoR_Normalized_Full_withNEC_nutrients %>%
filter(P_R=="R") %>%
mutate(Salinity=salinity) %>%
select(!c("salinity", "umol.cm2.hr", "SGD_number", "new_colonynumber", "sample_ID", "site", "date", "P_R", "ammonia_umolL")) ## removed ammonia for rn because have less than values and causing NAs
##visualize it
ggcorr(corr_matrix_envparams, method = c("everything", "pearson"))
###############################
### Dani B's methods #######
cor_mat <- round(cor(corr_matrix_envparams),4)
head(cor_mat)
## phosphate_umolL silicate_umolL NN_umolL temp_C new_pH
## phosphate_umolL 1.0000 0.2483 0.2789 0.1626 -0.0260
## silicate_umolL 0.2483 1.0000 0.1296 -0.1146 -0.1927
## NN_umolL 0.2789 0.1296 1.0000 0.1277 -0.0638
## temp_C 0.1626 -0.1146 0.1277 1.0000 0.2110
## new_pH -0.0260 -0.1927 -0.0638 0.2110 1.0000
## TA_initial 0.2644 0.4922 0.6274 -0.1274 -0.2502
## TA_initial Salinity
## phosphate_umolL 0.2644 -0.2635
## silicate_umolL 0.4922 -0.6914
## NN_umolL 0.6274 0.2955
## temp_C -0.1274 -0.1727
## new_pH -0.2502 -0.0230
## TA_initial 1.0000 0.0597
#melt the correlation matrix means it reassembles data frame to be more effective to complete corr matrix
#to long format
melted_cormat <- melt(cor_mat)
head(melted_cormat)
## Var1 Var2 value
## 1 phosphate_umolL phosphate_umolL 1.0000
## 2 silicate_umolL phosphate_umolL 0.2483
## 3 NN_umolL phosphate_umolL 0.2789
## 4 temp_C phosphate_umolL 0.1626
## 5 new_pH phosphate_umolL -0.0260
## 6 TA_initial phosphate_umolL 0.2644
#visulaize the correlation matrix in general
ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile()
# Get lower and upper triangle of the correlation matrix
#Note that, a correlation matrix has redundant information. We’ll use the functions below to set half of it to NA
get_lower_tri<-function(cor_mat){
cormat[upper.tri(cor_mat)] <- NA
return(cor_mat)
}
# Get upper triangle of the correlation matrix
get_upper_tri <- function(cor_mat){
cor_mat[lower.tri(cor_mat)]<- NA
return(cor_mat)
}
#apply upper tri calculation to graphc
upper_tri <- get_upper_tri(cor_mat)
upper_tri
## phosphate_umolL silicate_umolL NN_umolL temp_C new_pH
## phosphate_umolL 1 0.2483 0.2789 0.1626 -0.0260
## silicate_umolL NA 1.0000 0.1296 -0.1146 -0.1927
## NN_umolL NA NA 1.0000 0.1277 -0.0638
## temp_C NA NA NA 1.0000 0.2110
## new_pH NA NA NA NA 1.0000
## TA_initial NA NA NA NA NA
## Salinity NA NA NA NA NA
## TA_initial Salinity
## phosphate_umolL 0.2644 -0.2635
## silicate_umolL 0.4922 -0.6914
## NN_umolL 0.6274 0.2955
## temp_C -0.1274 -0.1727
## new_pH -0.2502 -0.0230
## TA_initial 1.0000 0.0597
## Salinity NA 1.0000
#melt the correlation matrix
#melt the correlation data and drop the rows with NA values
melted_cormat <- melt(upper_tri, na.rm = TRUE)
#heatmap of correlation matrix
#negative correlations are in purple color and positive correlations in red
#scale_fill_gradient2 is used with the argument limit = c(-1,1) as correlation coefficients range from -1 to 1
#coord_fixed() : this function ensures that one unit on the x-axis is the same length as one unit on the y-axis
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "midnightblue", high = "firebrick4", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
melted_cormat <- melted_cormat %>% mutate_at(vars(starts_with("value")), funs(round(., 2)))
# Create a ggheatmap with basic characteristics, etc.
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "firebrick3", mid = "white", high = "dodgerblue3",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+ # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
# Print the heatmap
print(ggheatmap)
#add correlation coefficients to the heatmap
#geom_text() to add the correlation coefficients on the graph
#guides() to change the position of the legend title
#if else statement in melted data frame to quotes of black and white to adjust text color
corr_matrix_SGD_params <- ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 6) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 18, face="bold", color="black"),
axis.text.y = element_text(size = 18, face="bold", color="black"),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(0.8, 0),
legend.title = element_text(size = 18, face="bold", color="black"),
legend.text = element_text(size = 20, face="bold", color="black"),
legend.position = c(0.48, 0.75),
legend.direction = "horizontal") +
guides(fill = guide_colorbar(barwidth = 12, barheight = 2,
title.position = "top", title.hjust = 0.5, title.vjust = 1.0))
corr_matrix_SGD_params
Results of back calculating
